Distributed Storage Networks and Computer Forensics

Transcription

1 Distributed Storage Networks and Computer Forensics 7 Storage Virtualization and DHT Technical Faculty Winter Semester 2011/12

2 Overview Concept of Virtualization Storage Area Networks Principles Optimization Distributed File Systems Without virtualization, e.g. Network File Systems With virtualization, e.g. Google File System Distributed Wide Area Storage Networks Distributed Hash Tables Peer-to-Peer Storage 2

3 Concept of Virtualization Principle A virtual storage constitutes handles all application accesses to the file system The virtual disk partitions files and stores blocks over several (physical) hard disks Control mechanisms allow redundancy and failure repair Control Virtualization server assigns data, e.g. blocks of files to hard disks (address space remapping) Controls replication and redundancy strategy Adds and removes storage devices 3 File Hard Disks Virtual Disk

4 Storage Virtualization Capabilities Replication Pooling Disk Management Advantages Data migration Higher availability Simple maintenance Scalability Disadvantages Un-installing is time consuming Compatibility and interoperability Complexity of the system Classic Implementation Host-based - Logical Volume Management - File Systems, e.g. NFS Storage devices based - RAID Network based - Storage Area Network New approaches Distributed Wide Area Storage Networks Distributed Hash Tables Peer-to-Peer Storage 4

5 Storage Area Networks Virtual Block Devices without file system connects hard disks Advantages simpler storage administration more flexible servers can boot from the SAN effective disaster recovery allows storage replication Compatibility problems between hard disks and virtualization server 5

6 SAN Networking Networking FCP (Fibre Channel Protocol) - SCSI over Fibre Channel iscsi (SCSI over TCP/IP) HyperSCSI (SCSI over Ethernet) ATA over Ethernet Fibre Channel over Ethernet iscsi over InfiniBand FCP over IP 6

7 SAN File Systems File system for concurrent read and write operations by multiple computers without conventional file locking concurrent direct access to blocks by servers Examples Veritas Cluster File System Xsan Global File System Oracle Cluster File System VMware VMFS IBM General Parallel File System 7

8 Distributed File Systems (without Virtualization) aka. Network File System Supports sharing of files, tapes, printers etc. Allows multiple client processes on multiple hosts to read and write the same files concurrency control or locking mechanisms necessary Examples Network File System (NFS) Server Message Block (SMB), Samba Apple Filing Protocol (AFP) Amazon Simple Storage Service (S3) 8

9 Distributed File Systems with Virtualization Example: Google File System File system on top of other file systems with builtin virtualization System built from cheap standard components (with high failure rates) Few large files Only operations: read, create, append, delete - concurrent appends and reads must be handled High bandwidth important Replication strategy chunk replication master replication Application GFS client (file name, chunk index) (chunk handle, chunk locations) (chunk handle, byte range) chunk data GFS master File namespace /foo/bar chunk 2ef0 Instructions to chunkserver GFS chunkserver Linux file system Chunkserver state Figure 1: GFS Architecture GFS chunkserver Linux file system 4 step 1 file region Client Master and replication decisions using global knowledge. However, tent 2 TCP connection to the chunkserver clients, over alta we must minimize its involvement in reads and writes 3 so period of time. Third, it reduces the dividual size of th o that it does not become a bottleneck. Clients never read stored on the master. This allows usorder to keep onth a and write file data through the master. Instead, a client asks in memory, which in turn brings other butadvanta undefi Secondary the master which chunkservers it should contact. It caches will discuss in Section Replica A this information for a limited time and interacts with the6 On the other hand, a large chunk size, even wit chunkservers directly for many subsequent operations. allocation, has its disadvantages. A3.2 small file Dac Let us explain the interactions for a simple read with reference to Figure 1. First, using the fixed chunk 7 size, the client storing those chunks may become hot small number of chunks, perhaps just one. WeThe deco c Primary use spots theifne m translates the file name and byte offset specified by the application into a chunk index within the file. Then, it sends been a major issue because our applications are5 accessing the same file. In practice, Replica client hottospo t Legend: pushed lin m the master a request containing the file name and chunk large multi-chunk files sequentially. in a pipel index. The master replies with the corresponding chunk However, hot spots did develop when GFS wa handle and locations of the replicas. The client caches this Control machine s 6 by a batch-queue system: an executable was wri information using the file name and chunk index Secondary as the key. as a single-chunk Data file and then started andon high-l hund The client then sends a request to one ofreplica the replicas, B chines at the same time. The few chunkservers through al most likely the closest one. The request specifies the chunk executable were overloaded by hundredsto of fully simu handle and a byte range within that chunk. Further reads quests. We fixed this problem by storing data is such pu of the same chunk require no more client-master Figure 2: interaction Write Control withand a higher Datareplication Flow factor andthan by making distr until the cached information expires or the file is reopened. queue system stagger application start eachtimes. mach The Google File System In fact, the client typically asks for multiple chunks in the long-term solution is to allow clients to ferread thedata Sanjay Ghemawat, same Howard request Gobioff, and theand master Shun-Tak can alsoleung include the information for chunks immediately following those requested. This clients in such situations. becomes unreachable Computer or repliesnetworks that it no longer and holds Telematics multiple r a lease. To avoid extra information sidesteps several future client-master interactions at practically no extra inter-switc 2.6 Metadata 9 3. cost. The client pushes the data tothe all master the replicas. stores three A client major types of metad machine fo can do so in any order. Each and chunk chunkserver Christian namespaces, will Schindelhauer the 2.5 Chunk Size store mapping from files and the locations of each chunk s replicas. network Allt the data in an internal LRU buffer cache until the Chunk size is one of the key design parameters. We have kept in the master s memory. The first client twois typ p chosen 64 MB, which is much larger data than is typical used or file aged sys- out. By decoupling the data flow paces and file-to-chunk mapping) aresends also kept thep from the control flow, we can improve performance by Legend: Data messages Control messages

10 Distributed Wide Area Storage Networks Distributed Hash Tables Relieving hot spots in the Internet Caching strategies for web servers Peer-to-Peer Networks Distributed file lookup and download in Overlay networks Most (or the best) of them use: DHT 10

11 WWW Load Balancing Web surfing: Web servers offer web pages Web clients request web pages Most of the time these requests are independent Requests use resources of the web servers bandwidth computation time Christian Stefan Arne 11

12 Load Some web servers have always high load for permanent high loads servers must be sufficiently powerful Some suffer under high fluctuations e.g. special events: - jpl.nasa.gov (Mars mission) - cnn.com (terrorist attack) Monday Tuesday Wednesday Server extension for worst case not reasonable Serving the requests is desired 12

13 Load Balancing in the WWW Monday Tuesday Wednesday Fluctuations target some servers A B A B A B (Commercial) solution Service providers offer exchange servers an Many requests will be distributed among these servers But how? A B 13

14 Literature Leighton, Lewin, et al. STOC 97 Consistent Hashing and Random Trees: Distributed Caching Protocols for Relieving Hot Spots on the World Wide Web Used by Akamai (founded 1997) Web-Cache 14

15 Start Situation Without load balancing Advantage simple Disadvantage servers must be designed for worst case situations Web-Server Web pages request Web-Clients 15

16 Site Caching Web-Server The whole web-site is copied to different web caches Browsers request at web server Web server redirects requests to Web- Cache Web-Cache delivers Web pages Advantage: good load balancing Disadvantage: bottleneck: redirect large overhead for complete web-site replication redirect Web-Cache Web-Clients 16

17 Proxy Caching Web-Server Each web page is distributed to a few web-caches Only first request is sent to web server Links reference to pages in the webcache Advantage: No bottleneck Disadvantages: Load balancing only implicit High requirements for placements redirect 2. Link 3. request Then, web clients surfs in the webcache Web- Cache Web-Client 17

18 Requirements Balance fair balancing of web pages Dynamics Efficient insert and delete of webcache-servers and files new X X Views Web-Clients see different set of web-caches 18

19 Hash Functions Buckets Items Set of Items: Set of Buckets: Example: 19

20 Ranged Hash-Funktionen Given: Items, Number Caches (Buckets), Bucket set: Views Ranged Hash Function: Prerequisite: for alle views Buckets View Items 20

21 First Idea: Hash Function Algorithm: Choose Hash function, e.g. 3 i + 1 mod n: number of Cache servers Balance: very good Dynamics Insert or remove of a single cache server New hash functions and total rehashing Very expensive!! i + 2 mod X

22 Requirements of the Ranged Hash Functions Monotony After adding or removing new caches (buckets) no pages (items) should be moved Balance All caches should have the same load Spread (Verbreitung,Streuung) A page should be distributed to a bounded number of Load caches No Cache should not have substantially more load than the average 22

23 Monotony After adding or removing new caches (buckets) no pages (items) should be moved Formally: For all Pages View 1: View 2: Caches Caches Pages 23

24 Balance For every view V the is the f V (i) balanced For a constant c and all : Pages View 1: View 2: Caches Caches Pages 24

25 Spread The spread σ(i) of a page i is the overall number of all necessary copies (over all views) View 1: View 2: View 3: 25

26 Load The load λ(b) of a cache b is the over-all number of all copies (over all views) wher!!!!! in View V := set of all pages assigned to bucket b View 1: View 2: λ(b 1 ) = 2 λ(b 2 ) = 3 View 3: b 1 b 2 26

27 Distributed Hash Tables Theorem There exists a family of hash function with the following properties Each function f F is monotone C! number of caches (Buckets) C/t! minimum number of caches per View V/C = constant (#Views / #Caches) I = C! (# pages = # Caches)! Balance: For every view! Spread: For each page i with probability! Load: For each cache b mit W keit 27

28 The Design 2 Hash functions onto the reals [0,1] maps k log C copies of cache b randomly to [0,1] maps web page i randomly to the interval [0,1] := Cache, which minimizes Caches (Buckets): View View Webseiten (Items): 28

29 Monotony := Cache which minimizes For all : Observe: blue interval in V 2 and in V 1 empty! View View

30 2. Balance Balance: For all views Caches (Buckets): Choose fixed view and a web page i Apply hash functions and. Under the assumption that the mapping is random every cache is chosen with the same probability View 0 1 Webseiten (Items): 30

31 3. Spread σ(i) = number of all necessary copies (over all views) C! number of caches (Buckets) C/t! minimum number of caches per View V/C = constant (#Views / #Caches) I = C! (# pages = # Caches) ever user knows at least a fraction of 1/t over the caches For every page i with prob. Proof sketch: Every view has a cache in an interval of length t/c (with high probability) The number of caches gives an upper bound for the spread 0 t/c 2t/C 1 31

32 4. Load Last (load): λ(b) = Number of copies over all views where := wet of pages assigned to bucket b under view V For every cache be we observe!!!!! with probability Proof sketch: Consider intervals of length t/c With high probability a cache of every view falls into one of these intervals The number of items in the interval gives an upper bound for the load 0 t/c 2t/C 1 32

33 Summary Distributed Hash Table is a distributed data structure for virtualization with fair balance provides dynamic behavior Standard data structure for dynamic distributed storages 33

34 Distributed Storage Networks and Computer Forensics 7 Storage Virtualization and DHT Technical Faculty Winter Semester 2011/12